Research Staff, LLMs

Location: EMEA - Remote

Company

Deepgram provides real-time APIs and self-hosted or on-premises software for speech-to-text, text-to-speech, and production-grade voice agents, powered by voice-native foundation models.

What you will do

• Define new LLM research initiatives with other Research Staff members.
• Survey, evaluate, classify, and distill current research methods.
• Design and conduct experimental programs for LLMs.
• Drive transformer training jobs on distributed compute infrastructure and deploy new models into production.
• Document and present research results and complex technical concepts clearly.
• Track advances in deep learning and LLMs, assessing their product implications and applications.

Requirements

• 3+ years of experience in applied deep learning research, including understanding of neural network types, architectures, and loss mechanisms.
• Proven experience with LLMs, including data curation, distributed large-scale training, transformer architecture optimization, and reinforcement learning.
• Strong Python and PyTorch coding experience.
• Experience with autoregressive and sequence-to-sequence transformer architectures.
• Experience with distributed computing and large-scale data processing.
• Experience conducting experimental programs and using results to optimize models.
• Strong communication, analytical, and technical explanation skills.

Nice to have

• Deep understanding of transformers, causal language models, and their underlying architecture.
• Understanding of distributed training and distributed inference schemes for LLMs.
• Familiarity with RLHF labeling and training pipelines.
• Up-to-date knowledge of recent LLM techniques and developments.
• Published deep learning research papers, particularly on LLMs and deep neural networks.

Culture & Benefits

• AI use and comfort are core expectations; team members actively experiment with advanced AI tools and integrate them into their workflows.
• The work evolves rapidly and requires continuous experimentation, adaptation, and learning.
• The role emphasizes pioneering new approaches, validating ideas through decisive experiments, and scaling successful proofs of concept.
• Full-time remote work in EMEA.